Overview

Dataset statistics

Number of variables15
Number of observations7209
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory844.9 KiB
Average record size in memory120.0 B

Variable types

Numeric14
Categorical1

Alerts

TIME is highly correlated with S and 13 other fieldsHigh correlation
S is highly correlated with TIME and 13 other fieldsHigh correlation
T1 is highly correlated with TIME and 13 other fieldsHigh correlation
T2 is highly correlated with TIME and 13 other fieldsHigh correlation
T3 is highly correlated with TIME and 13 other fieldsHigh correlation
T4 is highly correlated with TIME and 13 other fieldsHigh correlation
T5 is highly correlated with TIME and 13 other fieldsHigh correlation
T7 is highly correlated with TIME and 13 other fieldsHigh correlation
T8 is highly correlated with TIME and 13 other fieldsHigh correlation
T11 is highly correlated with TIME and 13 other fieldsHigh correlation
T12 is highly correlated with TIME and 13 other fieldsHigh correlation
T13 is highly correlated with TIME and 13 other fieldsHigh correlation
T14 is highly correlated with TIME and 13 other fieldsHigh correlation
Z is highly correlated with TIME and 13 other fieldsHigh correlation
T6 is highly correlated with TIME and 13 other fieldsHigh correlation
TIME is uniformly distributed Uniform
TIME has unique values Unique
S has 1217 (16.9%) zeros Zeros

Reproduction

Analysis started2022-11-11 03:27:24.854337
Analysis finished2022-11-11 03:27:35.527868
Duration10.67 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

TIME
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct7209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.3333333
Minimum0
Maximum600.6666667
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:35.554724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.03333333
Q1150.1666667
median300.3333333
Q3450.5
95-th percentile570.6333333
Maximum600.6666667
Range600.6666667
Interquartile range (IQR)300.3333333

Descriptive statistics

Standard deviation173.4336148
Coefficient of variation (CV)0.5774704156
Kurtosis-1.2
Mean300.3333333
Median Absolute Deviation (MAD)150.1666667
Skewness3.044298475 × 10-16
Sum2165103
Variance30079.21875
MonotonicityStrictly increasing
2022-11-11T11:27:35.611532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
403.58333331
 
< 0.1%
401.08333331
 
< 0.1%
4011
 
< 0.1%
400.91666671
 
< 0.1%
400.83333331
 
< 0.1%
400.751
 
< 0.1%
400.66666671
 
< 0.1%
400.58333331
 
< 0.1%
400.51
 
< 0.1%
Other values (7199)7199
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.083333333331
< 0.1%
0.16666666671
< 0.1%
0.251
< 0.1%
0.33333333331
< 0.1%
0.41666666671
< 0.1%
0.51
< 0.1%
0.58333333331
< 0.1%
0.66666666671
< 0.1%
0.751
< 0.1%
ValueCountFrequency (%)
600.66666671
< 0.1%
600.58333331
< 0.1%
600.51
< 0.1%
600.41666671
< 0.1%
600.33333331
< 0.1%
600.251
< 0.1%
600.16666671
< 0.1%
600.08333331
< 0.1%
6001
< 0.1%
599.91666671
< 0.1%

S
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10909.92912
Minimum0
Maximum20001
Zeros1217
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:35.664609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18000
median12001
Q315999
95-th percentile19999
Maximum20001
Range20001
Interquartile range (IQR)7999

Descriptive statistics

Standard deviation5855.888521
Coefficient of variation (CV)0.5367485396
Kurtosis-0.4068495776
Mean10909.92912
Median Absolute Deviation (MAD)3998
Skewness-0.6765456126
Sum78649679
Variance34291430.37
MonotonicityNot monotonic
2022-11-11T11:27:35.708218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
120011323
18.4%
01217
16.9%
15999988
13.7%
14000899
12.5%
9998765
10.6%
8000698
9.7%
18000427
 
5.9%
20001306
 
4.2%
7999250
 
3.5%
13999217
 
3.0%
Other values (6)119
 
1.7%
ValueCountFrequency (%)
01217
16.9%
91
 
< 0.1%
7999250
 
3.5%
8000698
9.7%
9998765
10.6%
114821
 
< 0.1%
116081
 
< 0.1%
120011323
18.4%
13999217
 
3.0%
14000899
12.5%
ValueCountFrequency (%)
20001306
 
4.2%
1999998
 
1.4%
18000427
 
5.9%
1799817
 
0.2%
15999988
13.7%
140681
 
< 0.1%
14000899
12.5%
13999217
 
3.0%
120011323
18.4%
116081
 
< 0.1%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct65
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.50735886
Minimum23
Maximum26.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:35.763779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile23.6
Q123.9
median24.4
Q325
95-th percentile25.9
Maximum26.2
Range3.2
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.6932028994
Coefficient of variation (CV)0.02828550002
Kurtosis-0.5928502996
Mean24.50735886
Median Absolute Deviation (MAD)0.5
Skewness0.4941812632
Sum176673.55
Variance0.4805302598
MonotonicityNot monotonic
2022-11-11T11:27:35.819591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.91086
15.1%
23.9852
11.8%
24.3705
9.8%
24.4649
9.0%
23.6504
 
7.0%
25.1456
 
6.3%
25.4399
 
5.5%
25.3378
 
5.2%
23.7369
 
5.1%
24365
 
5.1%
Other values (55)1446
20.1%
ValueCountFrequency (%)
2310
 
0.1%
23.051
 
< 0.1%
23.14
 
0.1%
23.151
 
< 0.1%
23.22
 
< 0.1%
23.251
 
< 0.1%
23.37
 
0.1%
23.351
 
< 0.1%
23.432
0.4%
23.451
 
< 0.1%
ValueCountFrequency (%)
26.244
 
0.6%
26.152
 
< 0.1%
26.1270
3.7%
26.052
 
< 0.1%
2638
 
0.5%
25.952
 
< 0.1%
25.914
 
0.2%
25.852
 
< 0.1%
25.822
 
0.3%
25.752
 
< 0.1%

T2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.31653489
Minimum23
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:35.869230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile23
Q123.2
median23.3
Q323.4
95-th percentile23.7
Maximum23.7
Range0.7
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.209058537
Coefficient of variation (CV)0.008966106587
Kurtosis-0.5575299173
Mean23.31653489
Median Absolute Deviation (MAD)0.1
Skewness0.4941659988
Sum168088.9
Variance0.0437054719
MonotonicityNot monotonic
2022-11-11T11:27:35.908899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
23.31893
26.3%
23.21331
18.5%
23.71066
14.8%
23.4861
11.9%
23.1800
11.1%
23727
 
10.1%
23.5414
 
5.7%
23.6117
 
1.6%
ValueCountFrequency (%)
23727
 
10.1%
23.1800
11.1%
23.21331
18.5%
23.31893
26.3%
23.4861
11.9%
23.5414
 
5.7%
23.6117
 
1.6%
23.71066
14.8%
ValueCountFrequency (%)
23.71066
14.8%
23.6117
 
1.6%
23.5414
 
5.7%
23.4861
11.9%
23.31893
26.3%
23.21331
18.5%
23.1800
11.1%
23727
 
10.1%

T3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.29747538
Minimum23
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:35.952507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile23
Q123.2
median23.3
Q323.4
95-th percentile23.7
Maximum23.7
Range0.7
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.2139988603
Coefficient of variation (CV)0.009185495715
Kurtosis-0.39355938
Mean23.29747538
Median Absolute Deviation (MAD)0.1
Skewness0.58558644
Sum167951.5
Variance0.04579551219
MonotonicityNot monotonic
2022-11-11T11:27:35.992257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
23.31722
23.9%
23.21647
22.8%
23.71109
15.4%
231102
15.3%
23.41062
14.7%
23.1484
 
6.7%
23.675
 
1.0%
23.58
 
0.1%
ValueCountFrequency (%)
231102
15.3%
23.1484
 
6.7%
23.21647
22.8%
23.31722
23.9%
23.41062
14.7%
23.58
 
0.1%
23.675
 
1.0%
23.71109
15.4%
ValueCountFrequency (%)
23.71109
15.4%
23.675
 
1.0%
23.58
 
0.1%
23.41062
14.7%
23.31722
23.9%
23.21647
22.8%
23.1484
 
6.7%
231102
15.3%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.63936746
Minimum23.1
Maximum24.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:36.036350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.1
5-th percentile23.2
Q123.4
median23.7
Q323.8
95-th percentile24.1
Maximum24.1
Range1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2591894637
Coefficient of variation (CV)0.01096431468
Kurtosis-0.6190262733
Mean23.63936746
Median Absolute Deviation (MAD)0.2
Skewness-0.08252249767
Sum170416.2
Variance0.06717917808
MonotonicityNot monotonic
2022-11-11T11:27:36.077912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
23.71669
23.2%
23.81003
13.9%
23.6887
12.3%
23.3867
12.0%
24.1659
 
9.1%
23.4557
 
7.7%
23.5467
 
6.5%
23.9372
 
5.2%
23.2261
 
3.6%
24257
 
3.6%
ValueCountFrequency (%)
23.1210
 
2.9%
23.2261
 
3.6%
23.3867
12.0%
23.4557
 
7.7%
23.5467
 
6.5%
23.6887
12.3%
23.71669
23.2%
23.81003
13.9%
23.9372
 
5.2%
24257
 
3.6%
ValueCountFrequency (%)
24.1659
 
9.1%
24257
 
3.6%
23.9372
 
5.2%
23.81003
13.9%
23.71669
23.2%
23.6887
12.3%
23.5467
 
6.5%
23.4557
 
7.7%
23.3867
12.0%
23.2261
 
3.6%

T5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.5313497
Minimum23.2
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:36.119032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.2
5-th percentile23.3
Q123.4
median23.5
Q323.7
95-th percentile23.8
Maximum23.8
Range0.6
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.1780610544
Coefficient of variation (CV)0.007566971579
Kurtosis-1.068804667
Mean23.5313497
Median Absolute Deviation (MAD)0.1
Skewness0.05359577082
Sum169637.5
Variance0.0317057391
MonotonicityNot monotonic
2022-11-11T11:27:36.221860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
23.41553
21.5%
23.51253
17.4%
23.81230
17.1%
23.61126
15.6%
23.3868
12.0%
23.7854
11.8%
23.2325
 
4.5%
ValueCountFrequency (%)
23.2325
 
4.5%
23.3868
12.0%
23.41553
21.5%
23.51253
17.4%
23.61126
15.6%
23.7854
11.8%
23.81230
17.1%
ValueCountFrequency (%)
23.81230
17.1%
23.7854
11.8%
23.61126
15.6%
23.51253
17.4%
23.41553
21.5%
23.3868
12.0%
23.2325
 
4.5%

T6
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.4 KiB
23.3
2670 
23.4
1832 
23.6
1365 
23.5
906 
23.7
436 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters28836
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row23.6
2nd row23.6
3rd row23.6
4th row23.6
5th row23.6

Common Values

ValueCountFrequency (%)
23.32670
37.0%
23.41832
25.4%
23.61365
18.9%
23.5906
 
12.6%
23.7436
 
6.0%

Length

2022-11-11T11:27:36.268704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:27:36.318016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
23.32670
37.0%
23.41832
25.4%
23.61365
18.9%
23.5906
 
12.6%
23.7436
 
6.0%

Most occurring characters

ValueCountFrequency (%)
39879
34.3%
27209
25.0%
.7209
25.0%
41832
 
6.4%
61365
 
4.7%
5906
 
3.1%
7436
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21627
75.0%
Other Punctuation7209
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
39879
45.7%
27209
33.3%
41832
 
8.5%
61365
 
6.3%
5906
 
4.2%
7436
 
2.0%
Other Punctuation
ValueCountFrequency (%)
.7209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common28836
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
39879
34.3%
27209
25.0%
.7209
25.0%
41832
 
6.4%
61365
 
4.7%
5906
 
3.1%
7436
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII28836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39879
34.3%
27209
25.0%
.7209
25.0%
41832
 
6.4%
61365
 
4.7%
5906
 
3.1%
7436
 
1.5%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.20145651
Minimum22.8
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:36.361849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.8
5-th percentile22.9
Q123.1
median23.1
Q323.3
95-th percentile23.7
Maximum23.7
Range0.9
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.2428485191
Coefficient of variation (CV)0.01046695146
Kurtosis0.008616794631
Mean23.20145651
Median Absolute Deviation (MAD)0.1
Skewness0.8780342809
Sum167259.3
Variance0.05897540325
MonotonicityNot monotonic
2022-11-11T11:27:36.401678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
23.12094
29.0%
23.21369
19.0%
23.3947
13.1%
23.7932
12.9%
22.9832
 
11.5%
23628
 
8.7%
23.6212
 
2.9%
22.8154
 
2.1%
23.530
 
0.4%
23.411
 
0.2%
ValueCountFrequency (%)
22.8154
 
2.1%
22.9832
 
11.5%
23628
 
8.7%
23.12094
29.0%
23.21369
19.0%
23.3947
13.1%
23.411
 
0.2%
23.530
 
0.4%
23.6212
 
2.9%
23.7932
12.9%
ValueCountFrequency (%)
23.7932
12.9%
23.6212
 
2.9%
23.530
 
0.4%
23.411
 
0.2%
23.3947
13.1%
23.21369
19.0%
23.12094
29.0%
23628
 
8.7%
22.9832
 
11.5%
22.8154
 
2.1%

T8
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.37301984
Minimum23
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:36.443165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile23
Q123.2
median23.4
Q323.5
95-th percentile23.7
Maximum23.7
Range0.7
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2036032103
Coefficient of variation (CV)0.008711035702
Kurtosis-0.8049997203
Mean23.37301984
Median Absolute Deviation (MAD)0.2
Skewness-0.1281186659
Sum168496.1
Variance0.04145426723
MonotonicityNot monotonic
2022-11-11T11:27:36.501969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
23.41361
18.9%
23.21160
16.1%
23.31093
15.2%
23.51079
15.0%
23.7809
11.2%
23.6751
10.4%
23672
9.3%
23.1284
 
3.9%
ValueCountFrequency (%)
23672
9.3%
23.1284
 
3.9%
23.21160
16.1%
23.31093
15.2%
23.41361
18.9%
23.51079
15.0%
23.6751
10.4%
23.7809
11.2%
ValueCountFrequency (%)
23.7809
11.2%
23.6751
10.4%
23.51079
15.0%
23.41361
18.9%
23.31093
15.2%
23.21160
16.1%
23.1284
 
3.9%
23672
9.3%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.99680954
Minimum24.9
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:36.554791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.9
5-th percentile25.1
Q125.8
median26
Q326.3
95-th percentile26.7
Maximum27
Range2.1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.4679387256
Coefficient of variation (CV)0.01799985205
Kurtosis-0.415407724
Mean25.99680954
Median Absolute Deviation (MAD)0.3
Skewness-0.383869902
Sum187411
Variance0.2189666509
MonotonicityNot monotonic
2022-11-11T11:27:36.608772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
26715
 
9.9%
25.9595
 
8.3%
26.1540
 
7.5%
25.8534
 
7.4%
26.3518
 
7.2%
26.2485
 
6.7%
26.5415
 
5.8%
26.4365
 
5.1%
26.6348
 
4.8%
25.2292
 
4.1%
Other values (33)2402
33.3%
ValueCountFrequency (%)
24.917
 
0.2%
24.952
 
< 0.1%
25199
2.8%
25.052
 
< 0.1%
25.1276
3.8%
25.154
 
0.1%
25.2292
4.1%
25.254
 
0.1%
25.3189
2.6%
25.3517
 
0.2%
ValueCountFrequency (%)
2747
 
0.7%
26.956
 
0.1%
26.964
 
0.9%
26.8510
 
0.1%
26.888
 
1.2%
26.7514
 
0.2%
26.7212
2.9%
26.6520
 
0.3%
26.6348
4.8%
26.5534
 
0.5%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.2521709
Minimum22.7
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:36.656989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.7
5-th percentile22.8
Q123.1
median23.2
Q323.4
95-th percentile23.6
Maximum23.7
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2306882182
Coefficient of variation (CV)0.009921147546
Kurtosis-0.6635364194
Mean23.2521709
Median Absolute Deviation (MAD)0.2
Skewness-0.1266169412
Sum167624.9
Variance0.05321705403
MonotonicityNot monotonic
2022-11-11T11:27:36.698500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
23.21563
21.7%
23.31068
14.8%
23.1905
12.6%
23.6816
11.3%
23.5732
10.2%
23.4715
9.9%
22.9587
 
8.1%
22.8338
 
4.7%
23332
 
4.6%
23.7125
 
1.7%
ValueCountFrequency (%)
22.728
 
0.4%
22.8338
 
4.7%
22.9587
 
8.1%
23332
 
4.6%
23.1905
12.6%
23.21563
21.7%
23.31068
14.8%
23.4715
9.9%
23.5732
10.2%
23.6816
11.3%
ValueCountFrequency (%)
23.7125
 
1.7%
23.6816
11.3%
23.5732
10.2%
23.4715
9.9%
23.31068
14.8%
23.21563
21.7%
23.1905
12.6%
23332
 
4.6%
22.9587
 
8.1%
22.8338
 
4.7%

T13
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.31733944
Minimum22.7
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:36.740361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.7
5-th percentile22.8
Q123.2
median23.4
Q323.5
95-th percentile23.6
Maximum23.7
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.229138645
Coefficient of variation (CV)0.009826963563
Kurtosis0.3166809714
Mean23.31733944
Median Absolute Deviation (MAD)0.1
Skewness-0.9923869564
Sum168094.7
Variance0.05250451865
MonotonicityNot monotonic
2022-11-11T11:27:36.781569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
23.41755
24.3%
23.51525
21.2%
23.31420
19.7%
23.6530
 
7.4%
23.2465
 
6.5%
22.8427
 
5.9%
22.9366
 
5.1%
23.1315
 
4.4%
23165
 
2.3%
23.7149
 
2.1%
ValueCountFrequency (%)
22.792
 
1.3%
22.8427
 
5.9%
22.9366
 
5.1%
23165
 
2.3%
23.1315
 
4.4%
23.2465
 
6.5%
23.31420
19.7%
23.41755
24.3%
23.51525
21.2%
23.6530
 
7.4%
ValueCountFrequency (%)
23.7149
 
2.1%
23.6530
 
7.4%
23.51525
21.2%
23.41755
24.3%
23.31420
19.7%
23.2465
 
6.5%
23.1315
 
4.4%
23165
 
2.3%
22.9366
 
5.1%
22.8427
 
5.9%

T14
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.15530587
Minimum22.4
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:36.822433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.4
5-th percentile22.6
Q123
median23.2
Q323.4
95-th percentile23.6
Maximum23.7
Range1.3
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2936290664
Coefficient of variation (CV)0.01268085458
Kurtosis-0.4815178802
Mean23.15530587
Median Absolute Deviation (MAD)0.2
Skewness-0.3281053925
Sum166926.6
Variance0.08621802866
MonotonicityNot monotonic
2022-11-11T11:27:36.868279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
23.21226
17.0%
23.11160
16.1%
23.5804
11.2%
23725
10.1%
23.4670
9.3%
23.3565
7.8%
22.7460
 
6.4%
23.6363
 
5.0%
22.6319
 
4.4%
22.8308
 
4.3%
Other values (4)609
8.4%
ValueCountFrequency (%)
22.428
 
0.4%
22.5130
 
1.8%
22.6319
 
4.4%
22.7460
 
6.4%
22.8308
 
4.3%
22.9257
 
3.6%
23725
10.1%
23.11160
16.1%
23.21226
17.0%
23.3565
7.8%
ValueCountFrequency (%)
23.7194
 
2.7%
23.6363
 
5.0%
23.5804
11.2%
23.4670
9.3%
23.3565
7.8%
23.21226
17.0%
23.11160
16.1%
23725
10.1%
22.9257
 
3.6%
22.8308
 
4.3%

Z
Real number (ℝ≥0)

HIGH CORRELATION

Distinct256
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.69295325
Minimum0
Maximum47
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size56.4 KiB
2022-11-11T11:27:36.920104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.3
Q112.3
median18.3
Q327.8
95-th percentile42.06
Maximum47
Range47
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation11.26124892
Coefficient of variation (CV)0.5718415504
Kurtosis-0.4052891834
Mean19.69295325
Median Absolute Deviation (MAD)7.6
Skewness0.3544731782
Sum141966.5
Variance126.8157273
MonotonicityNot monotonic
2022-11-11T11:27:36.975916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.5518
 
7.2%
18.3387
 
5.4%
25.9354
 
4.9%
29340
 
4.7%
15.2283
 
3.9%
3.3264
 
3.7%
3.3261
 
3.6%
15.1247
 
3.4%
19.4218
 
3.0%
12.1215
 
3.0%
Other values (246)4122
57.2%
ValueCountFrequency (%)
010
 
0.1%
0.91
 
< 0.1%
1.46
 
0.1%
1.580
1.1%
1.84
 
0.1%
1.948
0.7%
2.274
1.0%
2.322
 
0.3%
2.41
 
< 0.1%
2.71
 
< 0.1%
ValueCountFrequency (%)
4722
 
0.3%
46.644
 
0.6%
46.52
 
< 0.1%
46.415
 
0.2%
45.9145
2.0%
45.72
 
< 0.1%
45.61
 
< 0.1%
44.886
1.2%
44.736
 
0.5%
43.91
 
< 0.1%

Interactions

2022-11-11T11:27:34.626850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.343475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.082637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.792766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.543390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.242084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.008400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.663399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.414426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.072536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.839900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.564386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.218210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.940713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.672303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.394303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.133466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.840665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.593028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.291964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.054892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.712662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.461406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.123609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.887787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.610199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.265472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.991000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.722084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.446176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.187271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.893433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.646154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.344241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.104806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.764029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.511308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.242235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.936997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.660104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.315440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.043259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.768997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.495404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.238099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.942677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.696564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.395022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.152461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.813860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.559640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.294110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.984296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.707542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.363322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.092095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.816916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.546270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.291175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.994558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.748726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.447957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.201297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.930601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.609416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.345247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.036359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.756028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.414597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.143966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.866713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.598095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.344893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.045302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.801498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.501212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.250185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.981573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.658298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.398037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.086171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.805861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.464657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.195218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.910586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.643941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.393901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.092144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.849385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.614829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.295524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.028700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.702632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.445876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.131024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.850818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.509625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.242024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.957703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.693831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.445726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.142357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.900144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.665704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.342366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.078474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.750416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.496927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.178067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.898622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.557861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.291856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:35.002299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.739676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.494561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.188864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.947983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.713425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.388272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.125316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.795285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.545762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.223261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.943545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.602254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.338699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:35.125843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.790505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.547802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.306434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.999662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.766247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.438606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.176145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.844638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.598411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.273056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.993016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.651291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.390063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:35.170692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.839340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.596005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.353276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.047844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.813089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.483168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.222987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.889398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.646212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.317905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.037865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.696143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.436865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:35.214544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.885186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.644260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.401115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.095759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.861328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.528229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.270008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.934491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.694111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.362974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.082797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.804777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.484128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:35.258510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:25.932144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.693102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.447719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.144109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.910441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.572649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.316900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.979626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.741732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.407823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.127485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.849626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.530924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:35.305331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.037788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:26.744927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:27.497494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.194303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:28.961304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:29.620488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:30.366870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.027634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:31.793058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:32.520446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.175281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:33.896898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:34.579725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:27:37.031033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:27:37.105131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:27:37.243664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:27:37.321402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:27:37.399140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:27:35.382199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:27:35.490737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TIMEST1T2T3T4T5T6T7T8T11T12T13T14Z
00.000000023.023.023.123.123.223.623.023.025.0022.722.922.50.0
10.083333023.023.023.123.123.223.623.023.025.0022.722.922.50.0
20.166667023.023.023.123.123.223.623.023.025.0022.722.922.50.0
30.250000023.023.023.123.123.223.623.023.025.0022.722.922.50.0
40.333333023.023.023.123.123.223.623.023.025.0022.722.922.50.0
50.4166671160823.023.023.123.123.223.623.023.025.0022.722.922.50.0
60.5000001200123.023.023.123.123.223.623.023.025.0022.722.922.50.0
70.5833331200123.023.023.123.123.223.623.023.025.0022.722.922.50.0
80.6666671200123.023.023.123.123.223.623.023.025.0522.722.922.50.0
90.7500001200123.023.023.123.123.223.623.023.025.1022.722.922.50.0

Last rows

TIMEST1T2T3T4T5T6T7T8T11T12T13T14Z
7199599.916667023.523.623.623.723.723.723.623.625.022.822.722.41.9
7200600.000000023.523.623.623.723.723.723.623.625.022.822.722.41.9
7201600.083333023.523.623.623.723.723.723.623.625.022.822.722.41.9
7202600.166667023.523.623.623.723.723.723.623.625.022.822.722.41.9
7203600.250000023.523.623.623.723.723.723.623.625.022.822.722.41.9
7204600.333333023.523.623.623.723.723.723.623.625.022.822.722.41.9
7205600.416667023.523.623.623.723.723.723.623.625.022.822.722.41.9
7206600.500000023.523.623.623.723.723.723.623.625.022.822.722.41.9
7207600.583333023.523.623.623.723.723.723.623.625.022.822.722.41.9
7208600.666667023.523.623.623.723.723.723.623.625.022.822.722.41.9